Abstract
We developed new algorithms for Advanced Physiological Estimation of Cognitive Status (APECS),
which significantly improved the estimation of cognitive workload and shed new light on the estimation
of mental fatigue. More specifically, we used atomic decomposition to identify unique sources of
brain electrical activity as measured by the EEG recorded in human participants as they performed tasks
that induced different mental states, including engagement, mental workload, and mental fatigue. We tested
two types of atomic decomposition, each of which identifies unique EEG sources simultaneously in three
dimensions: 1) atoms with dimensions of power spectral density, space (electrode position), and
time (time on task or task conditions), or 2) atoms with dimensions of magnitude squared coherence,
spatial relationships (electrode pairs), and time. For tasks that induced mental workload, we found atoms that combine
sources in the theta (4-8 Hz) and alpha (8-12 Hz) EEG frequency bands consistently in individual participants at
different times of day and on different days. The temporal variations of the atoms clearly reflected the levels of
mental workload induced by varying task conditions. For a task that induced mental fatigue, we found atoms that
tracked the development of mental fatigue in individual participants over time, while reflecting underlying
changes in power or coherence of primarily theta-band EEG. Our results show that atomic decomposition is a
valuable new approach to the identification and measurement of EEG sources for monitoring cognitive status.
By comparing these results with results of prior analyses using the same data sets, we observed that atomic
decomposition can supplement or overcome existing approaches based on conventional two-dimensional spacetime or
frequency-time decomposition of EEG.
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